Acquiring user information needs for recommender systems

Most recommender systems attempt to use collaborative filtering, content-based filtering or hybrid approach to recommend items to new users. Collaborative filtering recommends items to new users based on their similar neighbours, and content-based filtering approach tries to recommend items that are similar to new users' profiles. The fundamental issues include how to profile new users, and how to deal with the over-specialization in content-based recommender systems. Indeed, the terms used to describe items can be formed as a concept hierarchy. Therefore, we aim to describe user profiles or information needs by using concepts vectors. This paper presents a new method to acquire user information needs, which allows new users to describe their preferences on a concept hierarchy rather than rating items. It also develops a new ranking function to recommend items to new users based on their information needs. The proposed approach is evaluated on Amazon book datasets. The experimental results demonstrate that the proposed approach can largely improve the effectiveness of recommender systems.

Slicing big data - Twitter, gambling and time sensitive information

Big Data presents many challenges related to volume, whether one is interested in studying past datasets or, even more problematically, attempting to work with live streams of data. The most obvious challenge, in a ‘noisy’ environment such as contemporary social media, is to collect the pertinent information; be that information for a specific study, tweets which can inform emergency services or other responders to an ongoing crisis, or give an advantage to those involved in prediction markets. Often, such a process is iterative, with keywords and hashtags changing with the passage of time, and both collection and analytic methodologies need to be continually adapted to respond to this changing information. While many of the data sets collected and analyzed are preformed, that is they are built around a particular keyword, hashtag, or set of authors, they still contain a large volume of information, much of which is unnecessary for the current purpose and/or potentially useful for future projects. Accordingly, this panel considers methods for separating and combining data to optimize big data research and report findings to stakeholders. The first paper considers possible coding mechanisms for incoming tweets during a crisis, taking a large stream of incoming tweets and selecting which of those need to be immediately placed in front of responders, for manual filtering and possible action. The paper suggests two solutions for this, content analysis and user profiling. In the former case, aspects of the tweet are assigned a score to assess its likely relationship to the topic at hand, and the urgency of the information, whilst the latter attempts to identify those users who are either serving as amplifiers of information or are known as an authoritative source. Through these techniques, the information contained in a large dataset could be filtered down to match the expected capacity of emergency responders, and knowledge as to the core keywords or hashtags relating to the current event is constantly refined for future data collection. The second paper is also concerned with identifying significant tweets, but in this case tweets relevant to particular prediction market; tennis betting. As increasing numbers of professional sports men and women create Twitter accounts to communicate with their fans, information is being shared regarding injuries, form and emotions which have the potential to impact on future results. As has already been demonstrated with leading US sports, such information is extremely valuable. Tennis, as with American Football (NFL) and Baseball (MLB) has paid subscription services which manually filter incoming news sources, including tweets, for information valuable to gamblers, gambling operators, and fantasy sports players. However, whilst such services are still niche operations, much of the value of information is lost by the time it reaches one of these services. The paper thus considers how information could be filtered from twitter user lists and hash tag or keyword monitoring, assessing the value of the source, information, and the prediction markets to which it may relate. The third paper examines methods for collecting Twitter data and following changes in an ongoing, dynamic social movement, such as the Occupy Wall Street movement. It involves the development of technical infrastructure to collect and make the tweets available for exploration and analysis. A strategy to respond to changes in the social movement is also required or the resulting tweets will only reflect the discussions and strategies the movement used at the time the keyword list is created — in a way, keyword creation is part strategy and part art. In this paper we describe strategies for the creation of a social media archive, specifically tweets related to the Occupy Wall Street movement, and methods for continuing to adapt data collection strategies as the movement’s presence in Twitter changes over time. We also discuss the opportunities and methods to extract data smaller slices of data from an archive of social media data to support a multitude of research projects in multiple fields of study. The common theme amongst these papers is that of constructing a data set, filtering it for a specific purpose, and then using the resulting information to aid in future data collection. The intention is that through the papers presented, and subsequent discussion, the panel will inform the wider research community not only on the objectives and limitations of data collection, live analytics, and filtering, but also on current and in-development methodologies that could be adopted by those working with such datasets, and how such approaches could be customized depending on the project stakeholders.

Product feature taxonomy learning based on user reviews

In recent years, the Web 2.0 has provided considerable facilities for people to create, share and exchange information and ideas. Upon this, the user generated content, such as reviews, has exploded. Such data provide a rich source to exploit in order to identify the information associated with specific reviewed items. Opinion mining has been widely used to identify the significant features of items (e.g., cameras) based upon user reviews. Feature extraction is the most critical step to identify useful information from texts. Most existing approaches only find individual features about a product without revealing the structural relationships between the features which usually exist. In this paper, we propose an approach to extract features and feature relationships, represented as a tree structure called feature taxonomy, based on frequent patterns and associations between patterns derived from user reviews. The generated feature taxonomy profiles the product at multiple levels and provides more detailed information about the product. Our experiment results based on some popularly used review datasets show that our proposed approach is able to capture the product features and relations effectively.

Predicting fault-prone software modules with rank sum classification

The detection and correction of defects remains among the most time consuming and expensive aspects of software development. Extensive automated testing and code inspections may mitigate their effect, but some code fragments are necessarily more likely to be faulty than others, and automated identification of fault prone modules helps to focus testing and inspections, thus limiting wasted effort and potentially improving detection rates. However, software metrics data is often extremely noisy, with enormous imbalances in the size of the positive and negative classes. In this work, we present a new approach to predictive modelling of fault proneness in software modules, introducing a new feature representation to overcome some of these issues. This rank sum representation offers improved or at worst comparable performance to earlier approaches for standard data sets, and readily allows the user to choose an appropriate trade-off between precision and recall to optimise inspection effort to suit different testing environments. The method is evaluated using the NASA Metrics Data Program (MDP) data sets, and performance is compared with existing studies based on the Support Vector Machine (SVM) and Naïve Bayes (NB) Classifiers, and with our own comprehensive evaluation of these methods.

Self-efficacy partially mediates between social support and health-related quality of life in family caregivers for dementia patients in Shanghai

Background: There is a paucity of research assessing health-related quality of life (HRQoL) and self-efficacy in caregivers of relatives with dementia in mainland China. Aims: To compare the level of HRQoL between caregivers and the general population in mainland China and to assess the role of caregiver self-efficacy in the relationship between caregiver social support and HRQoL. Methods: A cross-sectional study was conducted in Shanghai, China. The caregivers were recruited from the outpatient department of a teaching hospital. A total of 195 participants were interviewed, using a survey package including the Chinese version of the 36-Item Short-Form Health Survey (SF-36), demographic data, the variables associated with the impairments of care recipients, perceived social support and caregiver self-efficacy. The caregivers' SF-36 scores were compared with those of the general population in China. Results: The results indicated that the HRQoL of the caregivers was poorer compared with that of the general population when matched for age and gender. Multiple regression analyses revealed that caregiver self-efficacy is a partial mediator between social support and HRQoL, and a partial mediator between behavioral and psychological symptoms of dementia (BPSD) and caregiver mental health. Conclusion: Assisting with managing BPSD and enhancing caregiver self-efficacy can be considered integral parts of interventions to improve caregiver HRQoL.

Student focus and prioritization of design parameters in first-year engineering design projects

BACKGROUND Research on engineering design is a core area of concern within engineering education and a fundamental understanding of how engineering students approach and undertake design is necessary in order to develop effective design models and pedagogies. Understanding the factors related to design experiences in education and how they affect student practice can help educators as well as designers to leverage these factors as part of the design process. PURPOSE This study investigated the design practices of first-year engineering students’ and their experiences with a first-year engineering course design project. The research questions that guided the investigation were: 1. From a student perspective, what design parameters or criteria are most important? 2. How does this perspective impact subsequent student design practice throughout the design process? DESIGN/METHOD The authors employed qualitative multi-case study methods (Miles & Huberman, 1994) in order to the answer the research questions. Participant teams were observed and video recorded during team design meetings in which they researched the background for the design problem, brainstormed and sketched possible solutions, as well as built prototypes and final models of their design solutions as part of a course design project. Analysis focused on explanation building (Yin, 2009) and utilized within-case and cross-case analysis (Miles & Huberman, 1994). RESULTS We found that students focused disproportionally on the functional parameter, i.e. the physical implementation of their solution, and the possible/applicable parameter, i.e. a possible and applicable solution that benefited the user, in comparison to other given parameters such as safety and innovativeness. In addition, we found that individual teams focused on the functional and possible/ applicable parameters in early design phases such as brainstorming/ ideation and sketching. When prompted to discuss these non-salient parameters (from the student perspective) in the final design report, student design teams often used a post-hoc justification to support how the final designs fit the parameters that they did not initially consider. CONCLUSIONS This study suggests is that student design teams become fixated on (and consequently prioritize) certain parameters they interpret as important because they feel these parameters were described more explicitly in terms how they were met and assessed. Students fail to consider other parameters, perceived to be less directly assessable, unless prompted to do so. Failure to consider other parameters in the early design phases subsequently affects their approach in design phases as well. Case studies examining students’ study strategies within three Australian Universities illustrate similarities with some student approaches to design.

A Guide to Applying Road Safety within a Workplace : A Bilateral Approach to Organisational Road Safety in Australia and New Zealand

Corporate and organisational fleet and road safety is of strong interest to government and government agencies in Australia and New Zealand. It has been identified that there is great opportunity to engage and assist organisations and corporations in the delivery of road safety and road safety measures to achieve nationally significant road related trauma reductions. This guide has therefore been developed through public sector funding for use by any workplace within Australia and New Zealand. Significant road safety benefits can be achieved by road safety government agencies (Australia and New Zealand) that engage with private and public sectors at their workplaces to address work related road safety issues. It has also been noted that organisational road safety advancement creates effective and sustainable outcomes, safer places of employment, and safer communities. This can be achieved without totally relying upon traditional and often lengthy processes such as further public legislation and/ or community attitudinal and behavioural change programs. Currently, there is little in the way of robust guides or support for those organisations that are wishing to adopt road safety within their places of employment, supply chain and/ or community. Due to this identified gap in available resource and support, it has been recommended that a practical organisational road safety guide be produced; hence the development of this guide. A guide, or supporting documentation, that bridges the gap between government and road safety research knowledge, internationally endorsed road safety methodology, and assists industry as the end user. To achieve this, the guide is designed to be non-specific to any industry sector and usable for small or large organisations, public or private, and engaging for senior executives and the personnel on the ground responsible for its implementation. Therefore, this guide is based on methodology and principles so that it can be applicable in a scalable way to the greatest number of public and private organisations while providing enough detail and ‘how to’ advice to enable organisations to generate their own solutions to road safety issues.

From “anytime, anywhere” to “here and now”: place and time restrictions in mobile narratives to enhance situated engagement of mobile users

The usage of the mobile Internet has increased tremendously within the last couple of years, and thereby the vision of accessing information anytime, anywhere has become more realistic and a dominant design principle for providing content. However, this study challenges this paradigm of unlimited and unrestricted access, and explores the question whether constraints and restrictions can positively influence the motivation and enticement of mobile users to engage with location-specific content. Restrictions, such as a particular time or location that gives a user access to content, may be used to foster participation and engagement, as well as to support content production and to enhance the user’s experience. In order to explore this, a Mobile Narrative and a Narrative Map have been created. For the former, the access to individual chapters of the story was restricted. Authors can specify constraints, such as a location or time, which need to be met by the reader if they want to read the story. This concept allows creative writers of the story to exploit the fact that the reader’s context is known, by intensifying the user experience and integrating this knowledge into the writing process. The latter, the Narrative Map, provides users with extracts from stories or information snippets about authors at relevant locations. In both concepts, a feedback channel was also integrated, on which location, time, and size constraints were imposed. In a user-centred design process involving authors and potential readers, those concepts have been implemented, followed by an evaluation comprising four user studies. The results show that restrictions and constraints can indeed lead to more enticing and engaging user experiences, and restricted contribution opportunities can lead to a higher motivation to participate as well as to an improved quality of submissions. These findings are relevant for future developments in the area of mobile narratives and creative writing, as well as for common mobile services that aim for enticing user experiences.

Specification & design of safety technologies for complex socio-technical systems : low-cost level crossings case study

The introduction of safety technologies into complex socio-technical systems requires an integrated and holistic approach to HF and engineering, considering the effects of failures not only within system boundaries, but also at the interfaces with other systems and humans. Level crossing warning devices are examples of such systems where technically safe states within the system boundary can influence road user performance, giving rise to other hazards that degrade safety of the system. Chris will discuss the challenges that have been encountered to date in developing a safety argument in support of low-cost level crossing warning devices. The design and failure modes of level crossing warning devices are known to have a significant influence on road user performance; however, quantifying this effect is one of the ongoing challenges in determining appropriate reliability and availability targets for low-cost level crossing warning devices.

Cross-cultural web user interface design for Indigenous internet users

This project investigated 1) Australian web designers’ cultural perceptions towards Australian Indigenous users and 2) Australian Indigenous cultural features in terms of user interface design. In doing so, it reviews the literature of cross-cultural user interface design by focusing on feasible models and arguments to articulate and integrate Australian Indigenous Internet users’ cultural needs of web user interface. The online survey results collected from 101 Indigenous users and 126 Web designers showed a distinctive difference between them on the integration of Indigenous users' cultural in Web sites. The interview data collected from 14 Indigenous users and 14 web designers suggested practical approaches to the design implications of Indigenous culture.

Context-specific stressors, work-related social support and work-family conflict : a mediation study

Understanding the antecedents of work-family conflict is important as it allows organisations to effectively engage in work design for professional employees. This study examines the impact of sources of social support as antecedents of work-family conflict. The hypotheses were tests using Partial Least Squares modelling on a sample of 366 professional employees. The path model showed that context-specific stressors impacted positively on job demand, which led to higher levels of work-family conflict. Contrary to our expectation, non-work related social support did not have any statistical relationship with job demand and work-family conflict. In addition, individuals experiencing high job demands were found to obtain more social support from both work and non-work-related sources. Individuals with more work-related social support were less likely to have less work-family conflict. Surprisingly, non-work social support sources had no statistically significant relationship with work-family conflict.

Constructing a clinical decision-making framework for image-guided radiotherapy using a Bayesian Network

A decision-making framework for image-guided radiotherapy (IGRT) is being developed using a Bayesian Network (BN) to graphically describe, and probabilistically quantify, the many interacting factors that are involved in this complex clinical process. Outputs of the BN will provide decision-support for radiation therapists to assist them to make correct inferences relating to the likelihood of treatment delivery accuracy for a given image-guided set-up correction. The framework is being developed as a dynamic object-oriented BN, allowing for complex modelling with specific sub-regions, as well as representation of the sequential decision-making and belief updating associated with IGRT. A prototype graphic structure for the BN was developed by analysing IGRT practices at a local radiotherapy department and incorporating results obtained from a literature review. Clinical stakeholders reviewed the BN to validate its structure. The BN consists of a sub-network for evaluating the accuracy of IGRT practices and technology. The directed acyclic graph (DAG) contains nodes and directional arcs representing the causal relationship between the many interacting factors such as tumour site and its associated critical organs, technology and technique, and inter-user variability. The BN was extended to support on-line and off-line decision-making with respect to treatment plan compliance. Following conceptualisation of the framework, the BN will be quantified. It is anticipated that the finalised decision-making framework will provide a foundation to develop better decision-support strategies and automated correction algorithms for IGRT.

Professional conversations : a collaborative approach to support policy implementation, professional learning and practice change in ECEC

In 2012, Australia introduced a new National Quality Framework, comprising enhanced quality expectations for early childhood education and care services, two national learning frameworks and a new Assessment and Rating System spanning child care centres, kindergartens and preschools, family day care and outside school hours care. This is the linchpin in a series of education reforms designed to support increased access to higher quality early childhood education and care (ECEC) and successful transition to school. As with any policy change, success in real terms relies upon building shared understanding and the capacity of educators to apply new knowledge and to support change and improved practice within their service. With this in mind, a collaborative research project investigated the efficacy of a new approach to professional learning in ECEC: the professional conversation. This paper reports on the trial and evaluation of a series of professional conversations to support implementation of one element of the NQF, the Early Years Learning Framework (DEEWR,2009), and their capacity to promote collaborative reflective practice, shared understanding, and improved practice in ECEC. Set against the backdrop of the NQF, this paper details the professional conversation approach, key challenges and critical success factors, and the learning outcomes for conversation participants. Findings support the efficacy of this approach to professional learning in ECEC, and its capacity to support policy reform and practice change in ECEC.

Prediction of CO concentrations based on a hybrid Partial Least Square and Support Vector Machine model

Due to the health impacts caused by exposures to air pollutants in urban areas, monitoring and forecasting of air quality parameters have become popular as an important topic in atmospheric and environmental research today. The knowledge on the dynamics and complexity of air pollutants behavior has made artificial intelligence models as a useful tool for a more accurate pollutant concentration prediction. This paper focuses on an innovative method of daily air pollution prediction using combination of Support Vector Machine (SVM) as predictor and Partial Least Square (PLS) as a data selection tool based on the measured values of CO concentrations. The CO concentrations of Rey monitoring station in the south of Tehran, from Jan. 2007 to Feb. 2011, have been used to test the effectiveness of this method. The hourly CO concentrations have been predicted using the SVM and the hybrid PLS–SVM models. Similarly, daily CO concentrations have been predicted based on the aforementioned four years measured data. Results demonstrated that both models have good prediction ability; however the hybrid PLS–SVM has better accuracy. In the analysis presented in this paper, statistic estimators including relative mean errors, root mean squared errors and the mean absolute relative error have been employed to compare performances of the models. It has been concluded that the errors decrease after size reduction and coefficients of determination increase from 56 to 81% for SVM model to 65–85% for hybrid PLS–SVM model respectively. Also it was found that the hybrid PLS–SVM model required lower computational time than SVM model as expected, hence supporting the more accurate and faster prediction ability of hybrid PLS–SVM model.